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On Analysis and Visualization of Twitter Data

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Hybrid Intelligent Systems (HIS 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 420))

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Abstract

Provision of big data analysis in a customer-friendly applicable form, with ease and affordable cost to a wide range of customers and businesses is still a big challenge for data scientists and engineers. In this study, we focus on analysis of data and visualization of information and knowledge that eases application for customers. Present a framework for analysis. We analyze Twitter messages related to a one-year span in a specific geographical area, Okinawa Main Island. Our approach includes arranging data in a three-dimension framework of time, quality and volume. We map different elements of the data, such as number of tweets per user, time, span of time stayed in the Island, geographical location and content of messages. Based on the elements of the data within the framework, users are grouped and analyzed. A visual representation of analysis is presented.

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Author information

Authors and Affiliations

  1. Meio University, Nago-Shi, Okinawa, Japan

    Fathelalem Ali

  2. IVI, UKM, Bangi, Kuala Lumpur, Malaysia

    Yasuki Shima

Authors
  1. Fathelalem Ali
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  2. Yasuki Shima
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Corresponding author

Correspondence to Fathelalem Ali .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn, Washington, USA

    Ajith Abraham

  2. Department of Computer Science and Engineering, Chung-Ang University, Dongjak-gu, Korea (Republic of)

    Sang Yong Han

  3. Department of Computer Science, Gulf University for Science and Technology, Kuwait, Kuwait

    Salah A. Al-Sharhan

  4. Department of Computer, Shcol of Computer Science and Engineering, Dalian Maritime University, Dalian, China

    Hongbo Liu

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© 2016 Springer International Publishing Switzerland

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Ali, F., Shima, Y. (2016). On Analysis and Visualization of Twitter Data. In: Abraham, A., Han, S., Al-Sharhan, S., Liu, H. (eds) Hybrid Intelligent Systems. HIS 2016. Advances in Intelligent Systems and Computing, vol 420. Springer, Cham. https://doi.org/10.1007/978-3-319-27221-4_23

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  • DOI: https://doi.org/10.1007/978-3-319-27221-4_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27220-7

  • Online ISBN: 978-3-319-27221-4

  • eBook Packages: EngineeringEngineering (R0)

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